04.1_targeted-logFC
zagor
2024-11-28
1 libraries
library(scales)
library(ggplot2)
# packageVersion("ggplot2")
library(magrittr)
# library(crayon)
library(rstatix)
# library(ggpubr)
# library(Compositional)
# library(equalCovs)
# library(psych)
library(corrplot)
# library(pdist)
library(RColorBrewer)
library(plotly)
library(heatmaply)
# library(Hmisc)
library(htmlwidgets)
library(viridis)
library(ggh4x)
`%nin%` = Negate(`%in%`)2 f-ctions
3 predefined contrasts
4 metabolomics
fpi = file.path('..', 'input')
fn = "data_targeted.xlsx"
omics = openxlsx::read.xlsx(file.path(fpi, fn), sheet = 'metabolomics-leaves')
data.table::setDF(omics)
Genotype = 'Desiree'
omics = cbind(Genotype, omics)
PostInductionDay = as.numeric(stringr::str_split_i(gsub('S', '', omics$SampleID), '_', i = 2))
omics = cbind(PostInductionDay, omics)
omics = omics[omics$PostInductionDay %in% 1:14, ]
Condition = gsub('_.*', '', omics$SampleID)
omics = cbind(Condition, omics)
omics = omics[-which(omics$Condition == 'W' & omics$PostInductionDay %in% c(8,14)), ]
omics$Condition = factor(omics$Condition, levels = c('C', 'H', 'D', 'HD', 'W'))
table(omics$Condition, omics$PostInductionDay)##
## 1 7 8 14
## C 4 4 4 4
## H 4 4 4 4
## D 0 0 4 4
## HD 0 0 4 4
## W 4 4 0 0mygroup = paste(omics$Condition, stringr::str_pad(omics$PostInductionDay, 1, pad = "0"), sep = '_')
# table(mygroup %in% comp$V1)
# table(mygroup %in% comp$V3)
omics = cbind(mygroup, omics)4.1 palette
n = 6
# my.ggplot.palette = brewer.pal(8, "Set2")[c(8, 2, 6, 4, 5, 3)]#[c(8, 2, 6, 7, 5, 3)]
my.ggplot.palette = c('#7F7F7F', '#C00000', '#FFC000', '#ED7D31',
# '#70AD47',
'#4472C4')
pie(rep(1, length(my.ggplot.palette)),
col = my.ggplot.palette,
main = 'ggplot palette',
labels = c('C', 'H', 'D', 'HD',
# 'HDW',
'W'))
extend.palette = cbind(my.ggplot.palette, c('C', 'H', 'D', 'HD',
# 'HDW',
'W'))
colnames(extend.palette) = c('colour', 'group')
extend.palette = merge(extend.palette, omics[, 1:4], by.x = 'group', by.y = 'Condition', all = TRUE)
par(mfrow = c(2,2))
nn = 9
# (intervals = cut(c(-1,1), n*2))
intervals = cut(c(-1,1),
breaks = setdiff(seq(-1, 1, 0.1), 0),
include.lowest = TRUE)
my.heatmap.col1 = c(rev(brewer.pal(nn, 'Reds')), 'white', brewer.pal(nn, 'Blues'))
# my.heatmap.col1 = cividis(19, direction = -1)
pie(rep(1, length(my.heatmap.col1)),
col = rev(my.heatmap.col1),
labels = levels(intervals),
main = 'heatmap palette 1')
my.heatmap.col2 = c(my.heatmap.col1[1:3],
rep('grey90', length(4:(nn*2-2))),
my.heatmap.col1[(nn*2-1): (nn*2 + 1)])
pie(rep(1, length(my.heatmap.col1)),
col = rev(my.heatmap.col2),
labels = levels(intervals),
main = 'heatmap palette 2')
par(mfrow = c(1,1))
4.2 density
# column in which measurements start
nx = 6
data = omics
# colnames(data)[nx:ncol(data)]
data[, nx:ncol(data)] = apply(data[, nx:ncol(data)] , 2, as.numeric)
# https://www.cookbook-r.com/Manipulating_data/Converting_data_between_wide_and_long_format/
data.long = tidyr::gather(data, molecule, measurement, colnames(data)[nx]:colnames(data)[ncol(data)], factor_key=FALSE)
data.long$measurement = as.numeric(data.long$measurement)
data.long$molecule = factor(data.long$molecule, levels = colnames(data)[nx:ncol(data)])
data.long$PostInductionDay = ordered(data.long$PostInductionDay, levels = sort(unique(data.long$PostInductionDay )))
data.long = data.long[!is.na(data.long$measurement), ]
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = Condition, colour = Condition)) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = my.ggplot.palette) +
scale_fill_manual(name = 'Legend',
values = my.ggplot.palette) 
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = mygroup , colour = mygroup )) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]) +
scale_fill_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]
) 
4.3 plot measurements
cd = sort(unique(data.long.SE$PostInductionDay))
mypallette = my.ggplot.palette
# useful in case of more genotypes
lims = data.long.SE %>%
dplyr::group_by(molecule) %>%
dplyr::summarise(Panel = molecule,
ymin=min(mean-se),ymax=max(mean+se),
n = 5
)
lims = unique(lims)
lims = split(lims, lims$Panel) # make a list
lims = lapply(lims, function(l) {
scale_y_continuous(limits = c(l$ymin, l$ymax),
n.breaks = l$n)
}
)
title = 'Desiree'
data.SE = data.long.SE[data.long.SE$Genotype == title, ]
myggplot(data.SE, title, cnt, lncol = 2, lims, subdir = 'metabolomics')
4.4 correlation
mytitle = 'Desiree'
df = omics[omics$Genotype == mytitle, ]
df = as.matrix(df[, nx:ncol(df)])
df = apply(df, 2, as.numeric)
cs = colSums(is.na(df))/nrow(df)
if (any(cs == 1)) df = df[, -which(cs == 1)]
rownames(df) = omics$SampleID[omics$Genotype == mytitle]
mycond = levels(omics$Condition)
# Open the PDF device
pdf(file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
# Create the plot
my.pairs.panels(df, mytitle, mycond, cccex = 1)
# Close the device to save the plot
dev.off()## png
## 2
my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'C'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'H'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'D'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'W'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'HD'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') genotype = 'Desiree'
De.log2FC = NULL
De.p = NULL
for (i in 1:nrow(comp)) {
# print(i)
# print( as.character(comp[i, ]))
dtX = data.long[data.long$mygroup %in% comp[i, c(1,3)] & data.long$Genotype %in% genotype, ]
stress = dtX[dtX$mygroup == as.character(comp[i, 1]), ]
Ctrl = dtX[dtX$mygroup == as.character(comp[i, 3]), ]
StressC = gsub('_.*', '', comp[i, 1])
CtrlC = gsub('_.*', '', comp[i, 3])
log2FC = my.logFC(Ctrl = Ctrl, stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
p = my.customised.t.test(Ctrl = Ctrl, Stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
De.log2FC = rbind(De.log2FC, log2FC)
De.p = rbind(De.p, p)
}
Desiree.stat = merge(De.log2FC, De.p,
by.y = c("Genotype", "molecule", "group2", "group1"),
by.x = c("Genotype", "molecule", "stressGroup", "controlGroup"),
all = TRUE)
Desiree.stat$stressGroup = paste(gsub('_.*', '', Desiree.stat$stressGroup),
stringr::str_pad(gsub('.*_', '', Desiree.stat$stressGroup), 2, pad = "0"),
sep = '_')
logFC = tidyr::spread(Desiree.stat[, c(2:3, 7)], stressGroup, log2FC)
p = tidyr::spread(Desiree.stat[, c(2:3, 13)], stressGroup, p)
colnames(logFC)[2:ncol(logFC)] = paste(colnames(logFC)[2:ncol(logFC)], 'logFC', sep = ' | ')
colnames(p)[2:ncol(p)] = paste(colnames(p)[2:ncol(p)], 'p', sep = ' | ')
stat = merge(logFC, p, by = 'molecule')
ind = match(sort(colnames(stat)[2:ncol(stat)]), colnames(stat)[2:ncol(stat)])
stat = stat[, c(1, ind+1)]
stat[2:ncol(stat)] = format(round(as.data.frame(stat[2:ncol(stat)]), 3), nsmall = 3)
openxlsx::write.xlsx(stat, '../reports/Desiree.logFC-metabolomics_leaves.xlsx', asTable = TRUE)4.5 heatmap
comp = as.data.frame(comp)
forHeatmap = comp[intersect(grep('C', comp[, 1], invert = TRUE), grep('C', comp[, 3])), ]
forHeatmap$comparison = paste(forHeatmap[, 1], forHeatmap[, 3], sep = '-')
df = Desiree.stat[Desiree.stat$stressGroup %in% forHeatmap[, 1] & Desiree.stat$controlGroup %in% forHeatmap[, 3], ]
my.pheatmap.logFC(df, forHeatmap,
main = 'Desiree-metabolomics',
cluster_rows = FALSE, cluster_cols = FALSE,
subdir = 'metabolomics',
width = 6, height = 6, cnt = cnt)5 hormonomics
fpi = file.path('..', 'input')
fn = "data_targeted.xlsx"
omics = openxlsx::read.xlsx(file.path(fpi, fn), sheet = 'hormonomics-leaves')
data.table::setDF(omics)
Genotype = 'Desiree'
omics = cbind(Genotype, omics)
PostInductionDay = as.numeric(stringr::str_split_i(gsub('S|P', '', omics$SampleID), '_', i = 2))
omics = cbind(PostInductionDay, omics)
omics = omics[omics$PostInductionDay %in% 1:14, ]
Condition = gsub('_.*', '', omics$SampleID)
omics = cbind(Condition, omics)
omics = omics[-which(omics$Condition == 'W' & omics$PostInductionDay %in% c(8,14)), ]
omics$Condition = factor(omics$Condition, levels = c('C', 'H', 'D', 'HD', 'W'))
table(omics$Condition, omics$PostInductionDay)##
## 1 7 8 14
## C 4 4 4 4
## H 4 4 4 4
## D 0 0 4 4
## HD 0 0 4 4
## W 4 4 0 0mygroup = paste(omics$Condition, stringr::str_pad(omics$PostInductionDay, 1, pad = "0"), sep = '_')
# table(mygroup %in% comp$V1)
# table(mygroup %in% comp$V3)
omics = cbind(mygroup, omics)5.1 palette
my.ggplot.palette = c('#7F7F7F', '#C00000', '#FFC000', '#ED7D31',
# '#70AD47',
'#4472C4')
extend.palette = cbind(my.ggplot.palette, c('C', 'H', 'D', 'HD',
#'HDW',
'W'))
colnames(extend.palette) = c('colour', 'group')
extend.palette = merge(extend.palette, omics[, 1:4], by.x = 'group', by.y = 'Condition', all = TRUE)5.2 density
# column in which measurements start
nx = 6
data = omics
# colnames(data)[nx:ncol(data)]
data[, nx:ncol(data)] = apply(data[, nx:ncol(data)] , 2, as.numeric)
data.long = tidyr::gather(data, molecule, measurement, colnames(data)[nx]:colnames(data)[ncol(data)], factor_key=FALSE)
data.long$measurement = as.numeric(data.long$measurement)
data.long$molecule = factor(data.long$molecule, levels = colnames(data)[nx:ncol(data)])
data.long$PostInductionDay = ordered(data.long$PostInductionDay, levels = sort(unique(data.long$PostInductionDay )))
data.long = data.long[!is.na(data.long$measurement), ]
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = Condition, colour = Condition)) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = my.ggplot.palette) +
scale_fill_manual(name = 'Legend',
values = my.ggplot.palette) 
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = mygroup , colour = mygroup )) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]) +
scale_fill_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]
) 
5.3 plot measurements
cd = sort(unique(data.long.SE$PostInductionDay))
mypallette = my.ggplot.palette
lims = data.long.SE %>%
dplyr::group_by(molecule) %>%
dplyr::summarise(Panel = molecule,
ymin=min(mean-se),ymax=max(mean+se),
n = 5
)
lims = unique(lims)
lims = split(lims, lims$Panel) # make a list
lims = lapply(lims, function(l) {
scale_y_continuous(limits = c(l$ymin, l$ymax),
n.breaks = l$n)
}
)
title = 'Desiree'
data.SE = data.long.SE[data.long.SE$Genotype == title, ]
myggplot(data.SE, title, cnt, lncol = 2, lims, subdir = 'hormonomics')
5.4 correlation
mytitle = 'Desiree'
df = omics[omics$Genotype == mytitle, ]
df = as.matrix(df[, nx:ncol(df)])
df = apply(df, 2, as.numeric)
cs = colSums(is.na(df))/nrow(df)
if (any(cs == 1)) df = df[, -which(cs == 1)]
rownames(df) = omics$SampleID[omics$Genotype == mytitle]
mycond = levels(omics$Condition)
pdf(
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## png
## 2
my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'C'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'H'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'D'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'W'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'HD'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') genotype = 'Desiree'
De.log2FC = NULL
De.p = NULL
for (i in 1:nrow(comp)) {
# print(i)
# print( as.character(comp[i, ]))
dtX = data.long[data.long$mygroup %in% comp[i, c(1,3)] & data.long$Genotype %in% genotype, ]
stress = dtX[dtX$mygroup == as.character(comp[i, 1]), ]
Ctrl = dtX[dtX$mygroup == as.character(comp[i, 3]), ]
StressC = gsub('_.*', '', comp[i, 1])
CtrlC = gsub('_.*', '', comp[i, 3])
log2FC = my.logFC(Ctrl = Ctrl, stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
p = my.customised.t.test(Ctrl = Ctrl, Stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
De.log2FC = rbind(De.log2FC, log2FC)
De.p = rbind(De.p, p)
}
Desiree.stat = merge(De.log2FC, De.p,
by.y = c("Genotype", "molecule", "group2", "group1"),
by.x = c("Genotype", "molecule", "stressGroup", "controlGroup"),
all = TRUE)
Desiree.stat$stressGroup = paste(gsub('_.*', '', Desiree.stat$stressGroup),
stringr::str_pad(gsub('.*_', '', Desiree.stat$stressGroup), 2, pad = "0"),
sep = '_')
logFC = tidyr::spread(Desiree.stat[, c(2:3, 7)], stressGroup, log2FC)
p = tidyr::spread(Desiree.stat[, c(2:3, 13)], stressGroup, p)
colnames(logFC)[2:ncol(logFC)] = paste(colnames(logFC)[2:ncol(logFC)], 'logFC', sep = ' | ')
colnames(p)[2:ncol(p)] = paste(colnames(p)[2:ncol(p)], 'p', sep = ' | ')
stat = merge(logFC, p, by = 'molecule')
ind = match(sort(colnames(stat)[2:ncol(stat)]), colnames(stat)[2:ncol(stat)])
stat = stat[, c(1, ind+1)]
stat[2:ncol(stat)] = format(round(as.data.frame(stat[2:ncol(stat)]), 3), nsmall = 3)
openxlsx::write.xlsx(stat, '../reports/Desiree.logFC-hormonomics_leaves.xlsx', asTable = TRUE)5.5 heatmap
comp = as.data.frame(comp)
forHeatmap = comp[intersect(grep('C', comp[, 1], invert = TRUE), grep('C', comp[, 3])), ]
forHeatmap$comparison = paste(forHeatmap[, 1], forHeatmap[, 3], sep = '-')
df = Desiree.stat[Desiree.stat$stressGroup %in% forHeatmap[, 1] & Desiree.stat$controlGroup %in% forHeatmap[, 3], ]
my.pheatmap.logFC(df, forHeatmap,
main = 'Desiree-hormonomics',
cluster_rows = FALSE, cluster_cols = FALSE,
subdir = 'hormonomics',
width = 6, height = 6, cnt = cnt)6 transcriptomics
fpi = file.path('..', 'input')
fn = "data_targeted.xlsx"
omics = openxlsx::read.xlsx(file.path(fpi, fn), sheet = 'transcriptomics-leaves')
data.table::setDF(omics)
Genotype = 'Desiree'
omics = cbind(Genotype, omics)
PostInductionDay = as.numeric(stringr::str_split_i(gsub('S|P', '', omics$SampleID), '_', i = 2))
omics = cbind(PostInductionDay, omics)
omics = omics[omics$PostInductionDay %in% 1:14, ]
Condition = gsub('_.*', '', omics$SampleID)
omics = cbind(Condition, omics)
omics = omics[-which(omics$Condition == 'W' & omics$PostInductionDay %in% c(8,14)), ]
omics$Condition = factor(omics$Condition, levels = c('C', 'H', 'D', 'HD', 'W'))
table(omics$Condition, omics$PostInductionDay)##
## 1 7 8 14
## C 4 4 4 4
## H 4 4 4 4
## D 0 0 4 4
## HD 0 0 4 4
## W 4 4 0 0mygroup = paste(omics$Condition, stringr::str_pad(omics$PostInductionDay, 1, pad = "0"), sep = '_')
# table(mygroup %in% comp$V1)
# table(mygroup %in% comp$V3)
omics = cbind(mygroup, omics)6.1 palette
my.ggplot.palette = c('#7F7F7F', '#C00000', '#FFC000', '#ED7D31',
# '#70AD47',
'#4472C4')
extend.palette = cbind(my.ggplot.palette, c('C', 'H', 'D', 'HD',
#'HDW',
'W'))
colnames(extend.palette) = c('colour', 'group')
extend.palette = merge(extend.palette, omics[, 1:4], by.x = 'group', by.y = 'Condition', all = TRUE)6.2 density
# column in which measurements start
nx = 6
data = omics
# colnames(data)[nx:ncol(data)]
data[, nx:ncol(data)] = apply(data[, nx:ncol(data)] , 2, as.numeric)
data.long = tidyr::gather(data, molecule, measurement, colnames(data)[nx]:colnames(data)[ncol(data)], factor_key=FALSE)
data.long$measurement = as.numeric(data.long$measurement)
data.long$molecule = factor(data.long$molecule, levels = colnames(data)[nx:ncol(data)])
data.long$PostInductionDay = ordered(data.long$PostInductionDay, levels = sort(unique(data.long$PostInductionDay )))
data.long = data.long[!is.na(data.long$measurement), ]
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = Condition, colour = Condition)) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = my.ggplot.palette) +
scale_fill_manual(name = 'Legend',
values = my.ggplot.palette) 
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = mygroup , colour = mygroup )) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]) +
scale_fill_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]
) 
6.3 plot measurements
cd = sort(unique(data.long.SE$PostInductionDay))
mypallette = my.ggplot.palette
lims = data.long.SE %>%
dplyr::group_by(molecule) %>%
dplyr::summarise(Panel = molecule,
ymin=min(mean-se),ymax=max(mean+se),
n = 5
)
lims = unique(lims)
lims = split(lims, lims$Panel) # make a list
lims = lapply(lims, function(l) {
scale_y_continuous(limits = c(l$ymin, l$ymax),
n.breaks = l$n)
}
)
title = 'Desiree'
data.SE = data.long.SE[data.long.SE$Genotype == title, ]
myggplot(data.SE, title, cnt, lncol = 2, lims, subdir = 'transcriptomics')
6.4 correlation
mytitle = 'Desiree'
df = omics[omics$Genotype == mytitle, ]
df = as.matrix(df[, nx:ncol(df)])
df = apply(df, 2, as.numeric)
cs = colSums(is.na(df))/nrow(df)
if (any(cs == 1)) df = df[, -which(cs == 1)]
rownames(df) = omics$SampleID[omics$Genotype == mytitle]
mycond = levels(omics$Condition)
pdf(
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## png
## 2
my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'C'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'H'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'D'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'W'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'HD'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') genotype = 'Desiree'
De.log2FC = NULL
De.p = NULL
for (i in 1:nrow(comp)) {
# print(i)
# print( as.character(comp[i, ]))
dtX = data.long[data.long$mygroup %in% comp[i, c(1,3)] & data.long$Genotype %in% genotype, ]
stress = dtX[dtX$mygroup == as.character(comp[i, 1]), ]
Ctrl = dtX[dtX$mygroup == as.character(comp[i, 3]), ]
StressC = gsub('_.*', '', comp[i, 1])
CtrlC = gsub('_.*', '', comp[i, 3])
log2FC = my.logFC(Ctrl = Ctrl, stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
p = my.customised.t.test(Ctrl = Ctrl, Stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
De.log2FC = rbind(De.log2FC, log2FC)
De.p = rbind(De.p, p)
}
Desiree.stat = merge(De.log2FC, De.p,
by.y = c("Genotype", "molecule", "group2", "group1"),
by.x = c("Genotype", "molecule", "stressGroup", "controlGroup"),
all = TRUE)
Desiree.stat$stressGroup = paste(gsub('_.*', '', Desiree.stat$stressGroup),
stringr::str_pad(gsub('.*_', '', Desiree.stat$stressGroup), 2, pad = "0"),
sep = '_')
logFC = tidyr::spread(Desiree.stat[, c(2:3, 7)], stressGroup, log2FC)
p = tidyr::spread(Desiree.stat[, c(2:3, 13)], stressGroup, p)
colnames(logFC)[2:ncol(logFC)] = paste(colnames(logFC)[2:ncol(logFC)], 'logFC', sep = ' | ')
colnames(p)[2:ncol(p)] = paste(colnames(p)[2:ncol(p)], 'p', sep = ' | ')
stat = merge(logFC, p, by = 'molecule')
ind = match(sort(colnames(stat)[2:ncol(stat)]), colnames(stat)[2:ncol(stat)])
stat = stat[, c(1, ind+1)]
stat[2:ncol(stat)] = format(round(as.data.frame(stat[2:ncol(stat)]), 3), nsmall = 3)
openxlsx::write.xlsx(stat, '../reports/Desiree.logFC-transcriptomics_leaves.xlsx', asTable = TRUE)6.5 heatmap
comp = as.data.frame(comp)
forHeatmap = comp[intersect(grep('C', comp[, 1], invert = TRUE), grep('C', comp[, 3])), ]
forHeatmap$comparison = paste(forHeatmap[, 1], forHeatmap[, 3], sep = '-')
df = Desiree.stat[Desiree.stat$stressGroup %in% forHeatmap[, 1] & Desiree.stat$controlGroup %in% forHeatmap[, 3], ]
my.pheatmap.logFC(df, forHeatmap,
main = 'Desiree-transcriptomics',
cluster_rows = FALSE, cluster_cols = FALSE,
subdir = 'transcriptomics',
width = 6, height = 6, cnt = cnt)7 tubers metabolomics
fpi = file.path('..', 'input')
fn = "data_targeted.xlsx"
omics = openxlsx::read.xlsx(file.path(fpi, fn), sheet = 'metabolomics-tubers')
data.table::setDF(omics)
Genotype = 'Desiree'
omics = cbind(Genotype, omics)
PostInductionDay = as.numeric(stringr::str_split_i(gsub('S|P', '', omics$SampleID), '_', i = 2))
omics = cbind(PostInductionDay, omics)
Condition = gsub('_.*', '', omics$SampleID)
omics = cbind(Condition, omics)
omics$Condition = factor(omics$Condition, levels = c('C', 'H', 'D', 'HD', 'W'))
table(omics$Condition, omics$PostInductionDay)##
## 28
## C 12
## H 12
## D 12
## HD 12
## W 8mygroup = paste(omics$Condition, stringr::str_pad(omics$PostInductionDay, 1, pad = "0"), sep = '_')
# table(mygroup %in% comp$V1)
# table(mygroup %in% comp$V3)
omics = cbind(mygroup, omics)7.1 palette
my.ggplot.palette = c('#7F7F7F', '#C00000', '#FFC000', '#ED7D31',
# '#70AD47',
'#4472C4')
extend.palette = cbind(my.ggplot.palette, c('C', 'H', 'D', 'HD',
#'HDW',
'W'))
colnames(extend.palette) = c('colour', 'group')
extend.palette = merge(extend.palette, omics[, 1:4], by.x = 'group', by.y = 'Condition', all = TRUE)7.2 density
# column in which measurements start
nx = 9
data = omics
# colnames(data)[nx:ncol(data)]
data[, nx:ncol(data)] = apply(data[, nx:ncol(data)] , 2, as.numeric)
data.long = tidyr::gather(data, molecule, measurement, colnames(data)[nx]:colnames(data)[ncol(data)], factor_key=FALSE)
data.long$measurement = as.numeric(data.long$measurement)
data.long$molecule = factor(data.long$molecule, levels = colnames(data)[nx:ncol(data)])
data.long$PostInductionDay = ordered(data.long$PostInductionDay, levels = sort(unique(data.long$PostInductionDay )))
data.long = data.long[!is.na(data.long$measurement), ]
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = Condition, colour = Condition)) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = my.ggplot.palette) +
scale_fill_manual(name = 'Legend',
values = my.ggplot.palette) 
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = mygroup , colour = mygroup )) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]) +
scale_fill_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]
) 
7.3 plot measurements
cd = sort(unique(data.long.SE$PostInductionDay))
mypallette = my.ggplot.palette
title = 'Tubers'
lncol = 2
subdir = 'tubers-metabolomics'
# X11();
myplotY = ggplot(data.long, aes(x = Condition ,
y = measurement,
group = Condition,
shape = Condition,
fill = Condition,
colour = Condition)) +
geom_boxplot() +
facet_wrap(~ molecule, ncol = ncol,
strip.position = "top",
scales = 'free_y',
drop = FALSE
) +
ggtitle(title) +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = mypallette) +
scale_fill_manual(name = 'Legend',
values = mypallette
) +
labs(x = '',
y = 'Relative molecule abundance [+/- SE]') +
scale_y_continuous(# trans = log2_trans(),
labels = scales::comma
) +
theme(axis.text.x = element_text (angle = 0, # 45,
vjust = 1,
hjust = 1,
size = 12),
axis.text.y = element_text (angle = 0,
vjust = 1,
hjust = 1,
size = 12),
axis.title.x = element_text (angle = 0, # 45,
#vjust = 1,
#hjust = 1,
size = 12,
face = "bold"),
axis.title.y = element_text (angle = 90,
# vjust = 1,
# hjust = 1,
size = 12,
face = "bold"),
axis.text = element_text( angle = 90 ),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.position = "bottom",
legend.background = element_rect(fill=NULL,
size=0.1, linetype="solid"),
legend.title = element_text(color = NULL,
size = 12,
face = "bold"),
legend.text = element_text( size = 12),
strip.text = element_text(size = 12)) +
theme(
legend.box = "horizontal",
legend.key.width = unit(2, 'cm'),
legend.position = c(1, 0),
legend.justification = c(1, 0)) +
guides(colour = guide_legend(ncol = lncol)) +
guides(linetype = guide_legend(ncol = lncol)) +
guides(shape = "none") +
theme(panel.spacing.y = unit(1.5, "lines")) +
labs(color = "Legend", linetype = "Legend", shape = "Legend")
plot(myplotY) 
# dev.off()
fng = paste0(stringr::str_pad(cnt, 3, pad = "0"), '_', title, "_L.pdf")
ggsave(file.path('..', 'reports', subdir, fng),
width = 16, height = 12, units = "in")
fng = paste0(stringr::str_pad(cnt, 3, pad = "0"), '_', title, "_L.svg")
ggsave(file.path('..', 'reports', subdir, fng),
width = 16, height = 12, units = "in")7.4 correlation
mytitle = 'Desiree'
df = omics[omics$Genotype == mytitle, ]
df = as.matrix(df[, nx:ncol(df)])
df = apply(df, 2, as.numeric)
cs = colSums(is.na(df))/nrow(df)
if (any(cs == 1)) df = df[, -which(cs == 1)]
rownames(df) = omics$SampleID[omics$Genotype == mytitle]
mycond = levels(omics$Condition)
pdf(
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## png
## 2
my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'C'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'H'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'D'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'W'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'HD'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
pdf(
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.off()## pdf
## 3my.pairs.panels(df, mytitle, mycond, cccex = 1)
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') genotype = 'Desiree'
De.log2FC = NULL
De.p = NULL
comp = rbind(cbind('H_28', '-', 'C_28'),
cbind('D_28', '-', 'C_28'),
cbind('HD_28', '-', 'C_28'),
cbind('W_28', '-', 'C_28'))
for (i in 1:nrow(comp)) {
# print(i)
# print( as.character(comp[i, ]))
dtX = data.long[data.long$mygroup %in% comp[i, c(1,3)] & data.long$Genotype %in% genotype, ]
stress = dtX[dtX$mygroup == as.character(comp[i, 1]), ]
Ctrl = dtX[dtX$mygroup == as.character(comp[i, 3]), ]
StressC = gsub('_.*', '', comp[i, 1])
CtrlC = gsub('_.*', '', comp[i, 3])
log2FC = my.logFC(Ctrl = Ctrl, stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
p = my.customised.t.test(Ctrl = Ctrl, Stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
De.log2FC = rbind(De.log2FC, log2FC)
De.p = rbind(De.p, p)
}
Desiree.stat = merge(De.log2FC, De.p,
by.y = c("Genotype", "molecule", "group2", "group1"),
by.x = c("Genotype", "molecule", "stressGroup", "controlGroup"),
all = TRUE)
Desiree.stat$stressGroup = paste(gsub('_.*', '', Desiree.stat$stressGroup),
stringr::str_pad(gsub('.*_', '', Desiree.stat$stressGroup), 2, pad = "0"),
sep = '_')
logFC = tidyr::spread(Desiree.stat[, c(2:3, 7)], stressGroup, log2FC)
p = tidyr::spread(Desiree.stat[, c(2:3, 13)], stressGroup, p)
colnames(logFC)[2:ncol(logFC)] = paste(colnames(logFC)[2:ncol(logFC)], 'logFC', sep = ' | ')
colnames(p)[2:ncol(p)] = paste(colnames(p)[2:ncol(p)], 'p', sep = ' | ')
stat = merge(logFC, p, by = 'molecule')
ind = match(sort(colnames(stat)[2:ncol(stat)]), colnames(stat)[2:ncol(stat)])
stat = stat[, c(1, ind+1)]
stat[2:ncol(stat)] = format(round(as.data.frame(stat[2:ncol(stat)]), 3), nsmall = 3)
openxlsx::write.xlsx(stat, '../reports/Desiree.logFC-metabolomics_tubers.xlsx', asTable = TRUE)7.5 heatmap
comp = as.data.frame(comp)
forHeatmap = comp[intersect(grep('C', comp[, 1], invert = TRUE), grep('C', comp[, 3])), ]
forHeatmap$comparison = paste(forHeatmap[, 1], forHeatmap[, 3], sep = '-')
df = Desiree.stat[Desiree.stat$stressGroup %in% forHeatmap[, 1] & Desiree.stat$controlGroup %in% forHeatmap[, 3], ]
my.pheatmap.logFC(df, forHeatmap,
main = 'Desiree-tubers-metabolomics',
cluster_rows = FALSE, cluster_cols = FALSE,
subdir = 'tubers-metabolomics',
width = 6, height = 6, cnt = cnt)8 logFC results
9 Session info
## R version 4.4.1 (2024-06-14 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8
## [2] LC_CTYPE=English_United Kingdom.utf8
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.utf8
##
## time zone: Europe/Ljubljana
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggh4x_0.2.8 htmlwidgets_1.6.4 heatmaply_1.5.0 viridis_0.6.5
## [5] viridisLite_0.4.2 plotly_4.10.4 RColorBrewer_1.1-3 corrplot_0.95
## [9] rstatix_0.7.2 magrittr_2.0.3 ggplot2_3.5.1 scales_1.3.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 xfun_0.49 bslib_0.8.0 psych_2.4.6.26
## [5] lattice_0.22-6 crosstalk_1.2.1 vctrs_0.6.5 tools_4.4.1
## [9] generics_0.1.3 parallel_4.4.1 tibble_3.2.1 ca_0.71.1
## [13] fansi_1.0.6 pkgconfig_2.0.3 pheatmap_1.0.12 data.table_1.16.2
## [17] assertthat_0.2.1 webshot_0.5.5 lifecycle_1.0.4 farver_2.1.2
## [21] compiler_4.4.1 stringr_1.5.1 textshaping_0.4.0 mnormt_2.1.1
## [25] munsell_0.5.1 codetools_0.2-20 carData_3.0-5 seriation_1.5.6
## [29] htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.10 lazyeval_0.2.2
## [33] Formula_1.2-5 pillar_1.9.0 car_3.1-3 jquerylib_0.1.4
## [37] tidyr_1.3.1 cachem_1.1.0 iterators_1.0.14 TSP_1.2-4
## [41] abind_1.4-8 foreach_1.5.2 nlme_3.1-166 tidyselect_1.2.1
## [45] zip_2.3.1 digest_0.6.37 stringi_1.8.4 reshape2_1.4.4
## [49] dplyr_1.1.4 purrr_1.0.2 labeling_0.4.3 fastmap_1.2.0
## [53] grid_4.4.1 colorspace_2.1-1 cli_3.6.3 utf8_1.2.4
## [57] broom_1.0.7 withr_3.0.2 backports_1.5.0 registry_0.5-1
## [61] rmarkdown_2.29 httr_1.4.7 gridExtra_2.3 ragg_1.3.3
## [65] openxlsx_4.2.7.1 evaluate_1.0.1 knitr_1.49 rlang_1.1.4
## [69] Rcpp_1.0.13-1 dendextend_1.19.0 glue_1.8.0 svglite_2.1.3
## [73] rstudioapi_0.17.1 jsonlite_1.8.9 plyr_1.8.9 R6_2.5.1
## [77] systemfonts_1.1.0